Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Imaging of opaque specimens is performed using reflected rather than transmitted light. Illumination is supplied from above using orientations ranging from on‐axis (brightfield imaging) to highly oblique (oblique‐light microscopy; darkfield). The illuminating light might also be polarized (polarized light microscopy; differential interference contrast) or phase‐advanced or ‐retarded (phase contrast microscopy). Each of these techniques leads to generation of contrast from different features of a specimen, and several techniques can be used to complement each other and provide information about specimen composition, feature size, height, and other properties. Reflected‐light illumination is also the most common and most sensitive way to perform fluorescence microscopy on both transparent and opaque specimens. This article covers the principles behind the major techniques of reflected‐light optical microscopy and gives examples of materials applications, both traditional and emerging. The set‐up of the instrumentation required for each technique is given in detail, and Section “Practical Aspects of the Method” discusses the precise instrumentation and accessories needed to implement each technique. We also provide a guide to the most common imaging artifacts and pitfalls in reflected‐light microscopy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it